Forecasting Financial Crashes: Revisit to Log-Periodic Power Law

Bingcun Dai; Fan Zhang; Domenico Tarzia; Kwangwon Ahn

doi:10.1155/2018/4237471

Forecasting Financial Crashes: Revisit to Log-Periodic Power Law

Bingcun Dai, Fan Zhang, Domenico Tarzia, Kwangwon Ahn

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.

Original language	English
Article number	4237471
Journal	Complexity
Volume	2018
DOIs	https://doi.org/10.1155/2018/4237471
Publication status	Published - 2018

Bibliographical note

Funding Information:
This work was supported by KAIST through the 4th Industrial Revolution Project and Moon Soul Graduate School of Future Strategy (Kwangwon Ahn), and by Peking University HSBC Business School through Bairui Trust Research Fund (Kwangwon Ahn). In addition, the authors would like to thank Kyungmoo Heo, Hanwool Jang, and Guseon Ji for their excellent research assistant work.

Publisher Copyright:
© 2018 Bingcun Dai et al.

All Science Journal Classification (ASJC) codes

Computer Science(all)
General

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10.1155/2018/4237471

Cite this

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title = "Forecasting Financial Crashes: Revisit to Log-Periodic Power Law",

abstract = "We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.",

author = "Bingcun Dai and Fan Zhang and Domenico Tarzia and Kwangwon Ahn",

note = "Funding Information: This work was supported by KAIST through the 4th Industrial Revolution Project and Moon Soul Graduate School of Future Strategy (Kwangwon Ahn), and by Peking University HSBC Business School through Bairui Trust Research Fund (Kwangwon Ahn). In addition, the authors would like to thank Kyungmoo Heo, Hanwool Jang, and Guseon Ji for their excellent research assistant work. Publisher Copyright: {\textcopyright} 2018 Bingcun Dai et al.",

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doi = "10.1155/2018/4237471",

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AU - Tarzia, Domenico

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N1 - Funding Information: This work was supported by KAIST through the 4th Industrial Revolution Project and Moon Soul Graduate School of Future Strategy (Kwangwon Ahn), and by Peking University HSBC Business School through Bairui Trust Research Fund (Kwangwon Ahn). In addition, the authors would like to thank Kyungmoo Heo, Hanwool Jang, and Guseon Ji for their excellent research assistant work. Publisher Copyright: © 2018 Bingcun Dai et al.

PY - 2018

Y1 - 2018

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AB - We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.

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Forecasting Financial Crashes: Revisit to Log-Periodic Power Law

Abstract

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